Stupid is as stupid does.
This guide demonstrates how to replace a simple message list with a ContextManager that tracks user and assistant turns, estimates token usage, applies a sliding‑window truncation based on a token budget, and provides a single build_for_llm entry point to keep multi‑turn dialogues stable and observable.
This tutorial launches the Agent series by showing how to create a minimal, CLI‑driven entry point that wraps the model API in a Brain.chat() method, configures credentials via a .env file, handles errors gracefully, maintains multi‑turn conversation state, and outlines the project structure and next development steps.
The article explains how to transform a prototype LLM agent into a reliable production service by defining service contracts, externalizing configuration, handling session state, implementing multi‑level rate limiting, and integrating observability, deployment, and rollback mechanisms to avoid common engineering pitfalls.
The article explains why AI agents often produce unreliable answers after a tool call finishes, identifies the missing observation layer as the quality‑check station, and details how to format, classify, grade errors, perform self‑critique, enforce a Definition of Done, and prevent loops or premature termination.
The article explains that an Agent’s context is a structured snapshot built from role contracts, tool trajectories, and window budgeting, not a raw chat transcript, and details how proper context handling prevents forgetting, token bloat, and tool‑call mismatches in multi‑turn LLM workflows.
This guide walks you through Cursor AI’s advanced configuration—covering core features, custom Rules, the Model Context Protocol (MCP), user‑defined Commands, and switchable Modes—while showing practical Spring Boot and refactoring cases and quantifying efficiency gains.
This guide walks you through installing Claude Code on macOS and Windows, configuring it for mainland China using proxy‑free alternatives like OpenRouter, SiliconFlow, DeepSeek, DashScope or Ollama, and provides detailed tips on model selection, Skills, MCP setup, performance tuning, security, and troubleshooting.
This tutorial walks beginners through Python fundamentals, LangChain installation, core components like prompt templates, chains, and retrieval, and culminates in a complete intelligent customer‑service chatbot, showing step‑by‑step code and practical tips for building AI applications.
This guide explains what an AI Agent is, outlines its four core abilities, compares it with traditional programs, presents a layered architecture diagram, shows common use‑case scenarios, and provides step‑by‑step Java code using LangChain4j to build basic, multi‑tool, planning, reflective, and collaborative agents together with optimization tips and FAQs.
This guide walks you through setting up Spring AI, configuring OpenAI credentials, using ChatClient for basic and advanced interactions, enabling function calling, managing conversation memory, and building real‑world projects such as a smart customer‑service bot, document summarizer, and code assistant, while also comparing Spring AI with LangChain4j and addressing common setup issues.
